Topic
Tone mapping
About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.
Papers published on a yearly basis
Papers
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15 Mar 2010TL;DR: In this paper, an HDR signal processor is used to generate an HDR image from the pixel data S1 to S3, and performs tone mapping by multiplying this image data by the gain.
Abstract: An imaging control apparatus includes an HDR signal processor and a frame memory. The HDR signal processor obtains pixel data S1 and S2 obtained by imaging with a very short exposure time T1 and a short exposure time T2 earlier than obtaining pixel data S3 obtained by imaging with a normal exposure time T3, generates a luminance image data by separating illumination light component from the pixel data S1 and S2, the generation of the luminance image data starting when the pixel data S2 is obtained, and generates a gain for tone mapping from the luminance image data. On the other hand, the HDR signal processor generates an HDR image data from the pixel data S1 to S3, the generation of the HDR image starting when the pixel data S3 is obtained, and performs tone mapping by multiplying this image data by the gain.
47 citations
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15 Jun 2015TL;DR: A broad review of the HDR methods and technologies with an introduction to fundamental concepts behind the perception of HDR imagery and image and video quality metrics suitable for HDR content are offered.
Abstract: High dynamic range (HDR) images and video contain pixels, which can represent much greater range of colors and brightness levels than that offered by existing, standard dynamic range images. Such “better pixels” greatly improve the overall quality of visual content, making it appear much more realistic and appealing to the audience. HDR is one of the key technologies of the future imaging pipeline, which will change the way the digital visual content is represented and manipulated.
This article offers a broad review of the HDR methods and technologies with an introduction to fundamental concepts behind the perception of HDR imagery. It serves as both an introduction to the subject and a review of the current state of the art in HDR imaging. It covers the topics related to capture of HDR content with cameras and its generation with computer graphics methods; encoding and compression of HDR images and video; tone mapping for displaying HDR content on standard dynamic range displays; inverse tone mapping for upscaling legacy content for presentation on HDR displays; the display technologies offering HDR range; and finally image and video quality metrics suitable for HDR content.
Keywords:
high dynamic range imaging;
tone mapping
47 citations
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19 Oct 2017TL;DR: This work develops an online keyframe strategy to keep track of the dynamic objects, where more temporal information can be acquired than a single previous frame, and proposes a video quality metric to evaluate temporal coherence.
Abstract: Image color editing techniques such as color transfer, HDR tone mapping, dehazing, and white balance have been widely used and investigated in recent decades. However, naively employing them to videos frame-by-frame often leads to flickering or color inconsistency. To solve it generally, earlier methods rely on temporal filtering or warping from the previous frame, but they still fail in the cases of occlusion and produce blurry results. We introduce a new framework for these challenges: (1) We develop an online keyframe strategy to keep track of the dynamic objects, where more temporal information can be acquired than a single previous frame. (2) To preserve image details, local color affine model is employed. The main concept of this post-processing step is to capture the color transformation from editing algorithms and maintain the detail structures of the raw image simultaneously. Practically, our approach takes a raw video and its per-frame processed version, and generates a temporally consistent output. In addition, we propose a video quality metric to evaluate temporal coherence. Extensive experiments and subjective test are done to show the superiority of the proposed framework with respect to color fidelity, detail preservation, and temporal consistency.
46 citations
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01 Oct 2004TL;DR: This method improves the results of the original Multi Scale Retinex algorithm in a way that retains the global brightness contrast and the natural impression of the resulting image by recombining the original picture in a certain weight.
Abstract: This paper presents a new method of compressing the dynamic range of wide dynamic range scenes. This method is based on the Multi Scale Retinex algorithm. It improves the results of the original Multi Scale Retinex algorithm in a way that retains the global brightness contrast and the natural impression of the resulting image by recombining the original picture in a certain weight. Further improvement of the global brightness contrast is achieved by adjusting the histogram of the resulting picture. The paper explores the performance of this modified algorithm on different wide dynamic range scenes and points out its advantages over other dynamic range compression algorithms.
46 citations
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TL;DR: A distortion-free data hiding algorithm which can embed secret messages into high dynamic range (HDR) images and performs with adaptive message embedding where pixels conceal different amounts of secret messages based on their homogeneous representations.
46 citations